# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import torch.nn.functional as F


class ConvBN(nn.Sequential):
    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
                 bias=False):
        super(ConvBN, self).__init__(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
            norm_layer(out_channels)
        )


class Conv(nn.Sequential):
    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
        super(Conv, self).__init__(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
        )


class ConvBNRelu(nn.Module):
    def __init__(self, inChannel, outChannel, kernel_size=3, padding=1, bias=True):
        super(ConvBNRelu, self).__init__()
        self.conv = nn.Conv2d(inChannel, outChannel, kernel_size=kernel_size, padding=padding, bias=bias)
        self.bn = nn.BatchNorm2d(outChannel, momentum=0.1, affine=True)
        self.reLu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return self.reLu(x)


try:
    import os, sys

    kernel_path = os.path.abspath(os.path.join('..'))
    sys.path.append(kernel_path)
    from kernels.window_process.window_process import WindowProcess, WindowProcessReverse

except:
    WindowProcess = None
    WindowProcessReverse = None
    print("[Warning] Fused window process have not been installed. Please refer to get_started.md for installation.")


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B,C, H, W = x.shape
    x = x.view(B,C, H // window_size, window_size, W // window_size, window_size)
    x=x.permute(0, 2, 4, 1, 3, 5) #B, H // window_size, W // window_size,C, window_size, window_size

    windows = x.reshape(-1,C,window_size, window_size) # -1,C,winH,winW
    return windows

def window_reverse(windows, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    _,C,window_size,_=windows.shape

    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, C, window_size, window_size)
    x= x.permute(0,3, 1, 4, 2, 5)#B, C, H // window_size, window_size, W // window_size, window_size
    x=x.reshape(B,C,H,W)
    return x
# 对transformer进行简化

class WinLearnedAttention(nn.Module):
    def __init__(self, H, ):
        super(WinLearnedAttention, self).__init__()
        self.attn = nn.Sequential(
            nn.Linear(H*H,H* H, bias=True),
            # nn.BatchNorm2d(H),
            nn.ReLU(inplace=True),
        )
        # self.attnH=nn.Sequential(
        #     nn.Linear(H,H, bias=True),
        #     # nn.BatchNorm2d(H),
        #     nn.ReLU(inplace=True),
        # )
        #self.norm= nn.BatchNorm2d(H)
        # self.attnChannel = nn.Sequential(
        #     nn.Conv2d(inChannel, outChannel, kernel_size=1, stride=1, padding=0, bias=True),
        #     nn.BatchNorm2d(outChannel),
        #     nn.ReLU(inplace=True),
        # )

    def forward(self, x):
        B, C, H, W = x.shape

        x = x.reshape(B, C, H * W)
        x = self.attn(x)
        x = x.reshape(B, C, H, W)

        return x
class LineLearnedAttention(nn.Module):
    def __init__(self, H, ):
        super(LineLearnedAttention, self).__init__()
        self.attnH = nn.Sequential(
            nn.Linear(H, H, bias=True),
            # nn.BatchNorm2d(H),
            nn.ReLU(inplace=True),
        )
        self.attnW = nn.Sequential(
            nn.Linear(H, H, bias=True),
            # nn.BatchNorm2d(H),
            nn.ReLU(inplace=True),
        )
        # self.attnH=nn.Sequential(
        #     nn.Linear(H,H, bias=True),
        #     # nn.BatchNorm2d(H),
        #     nn.ReLU(inplace=True),
        # )
        #self.norm= nn.BatchNorm2d(H)
        # self.attnChannel = nn.Sequential(
        #     nn.Conv2d(inChannel, outChannel, kernel_size=1, stride=1, padding=0, bias=True),
        #     nn.BatchNorm2d(outChannel),
        #     nn.ReLU(inplace=True),
        # )

    def forward(self, x):
        B, C, H, W = x.shape

        x = self.attnW(x)
        x=x.transpose(-1,-2)
        x=self.attnH(x)
        x = x.transpose(-1, -2)
        x = self.attnW(x)
        x = x.transpose(-1, -2)
        x = self.attnH(x)
        x = x.transpose(-1, -2)
        # x = x.reshape(B, C, H, W)

        return x
class block(nn.Module):
    def __init__(self, window_size,H,W,dimIn,dimOut ):
        super(block, self).__init__()
        self.window_size=window_size
        self.attn1=LearnedAttention(H)
        self.attn2 = LearnedAttention(H)
        self.H=H
        self.W=W
        self.conv=nn.Sequential(ConvBNRelu(dimIn, dimOut),
                                         ConvBNRelu(dimOut, dimOut))
    def forward(self, x):
        x = window_partition(x, self.window_size)
        x = self.attn1(x)
        x=window_reverse(x, self.H, self.W)
        # 使用卷积让window之间的像素交流
        x = self.conv(x)
        x = window_partition(x, self.window_size)
        x = self.attn2(x)
        x=window_reverse(x, self.H, self.W)
        return x
class LAttnBlock(nn.Module):
    def __init__(self, winSize,inDim=32,outDim=32):
        super(LAttnBlock, self).__init__()
        self.winAttn1 = WinLearnedAttention(winSize)
        self.winAttn2 = WinLearnedAttention(winSize)
        # self.winAttn3 = WinLearnedAttention(winSize)
        # self.winAttn4 = WinLearnedAttention(winSize)
        self.winSize=winSize

        self.attnChannel1 = nn.Sequential(
            nn.Conv2d(inDim, outDim, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(outDim),
            nn.ReLU(inplace=True),
        )
        self.attnChannel2 = nn.Sequential(
            nn.Conv2d(outDim, outDim, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(outDim),
            nn.ReLU(inplace=True),
        )
        # self.attnChannel3 = nn.Sequential(
        #     nn.Conv2d(outDim, outDim, kernel_size=1, stride=1, padding=0, bias=True),
        #     nn.BatchNorm2d(outDim),
        #     nn.ReLU(inplace=True),
        # )
        # self.attnChannel4 = nn.Sequential(
        #     nn.Conv2d(outDim, outDim, kernel_size=1, stride=1, padding=0, bias=True),
        #     nn.BatchNorm2d(outDim),
        #     nn.ReLU(inplace=True),
        # )
    def forward(self, x):
        B,C,H,W=x.shape

        x=window_partition(x,self.winSize)#切分窗口
        x=self.winAttn1(x)#窗口化attention
        x=window_reverse(x,H,W)#拉回原始大小

        x = self.attnChannel1(x)

        if self.winSize<=(H//2):
            #使用roll
            shift_size=self.winSize // 2
            x = torch.roll(x, shifts=(-shift_size, -shift_size), dims=(2, 3))
            x = window_partition(x, self.winSize)  # 切分窗口
            x = self.winAttn2(x)  # 窗口化attention
            x = window_reverse(x, H, W)  # 拉回原始大小
            x = torch.roll(x, shifts=(shift_size, shift_size), dims=(2, 3))

            x=self.attnChannel2(x)

        # x = window_partition(x, self.winSize)  # 切分窗口
        # x = self.winAttn3(x)  # 窗口化attention
        # x = window_reverse(x, H, W)  # 拉回原始大小

        # x = self.attnChannel3(x)
        #
        # if self.winSize <= (H // 2):
        #     # 使用roll
        #     shift_size = self.winSize // 2
        #     x = torch.roll(x, shifts=(-shift_size, -shift_size), dims=(2, 3))
        #     x = window_partition(x, self.winSize)  # 切分窗口
        #     x = self.winAttn4(x)  # 窗口化attention
        #     x = window_reverse(x, H, W)  # 拉回原始大小
        #     x = torch.roll(x, shifts=(shift_size, shift_size), dims=(2, 3))
        #     # #使用pading
        #     # padSize = self.winSize // 2
        #     # x = F.pad(x, (padSize, padSize, padSize, padSize), "constant",0)  # 上下左右都填充winSize一半
        #     # padB,padC,padH,padW=x.shape
        #     # x = window_partition(x, self.winSize)  # 切分窗口
        #     # x = self.winAttn2(x)  # 窗口化attention
        #     # x = window_reverse(x, padH, padW)  # 拉回原始大小
        #     # x = x[:, :, padSize:-padSize, padSize:-padSize]  # 切掉填充的数值
        #     # 使用shiftpooling
        #     #     x=shiftPooling(x,self.winSize)#shift pooling
        #     # x = window_partition(x, self.winSize)  # 切分窗口
        #     # x = self.winAttn1(x)  # 窗口化attention
        #     # x = window_reverse(x, H, W)  # 拉回原始大小`
        #     x = self.attnChannel4(x)

        return x

class Lite_WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()

        self.attn = nn.Sequential(
            nn.Linear(window_size*window_size, window_size*window_size, bias=True),
            # nn.BatchNorm2d(dim),
            nn.ReLU(inplace=True),
        )
        # self.ChannelAttn=nn.Conv2d(dim, dim, kernel_size=1, bias=False)
        # self.Win_size=window_size
    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, C, N = x.shape

        # shutcut=x
        # q = q * self.scale

        torch.cuda.empty_cache()


        x=self.attn(x)

        # x=x.reshape(B_,C,self.Win_size,self.Win_size)
        # x=self.ChannelAttn(x)
        # x=x.reshape(B_,C,self.Win_size*self.Win_size)

        # x=x+shutcut

        return x

    # def extra_repr(self) -> str:
    #     return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
    #
    # def flops(self, N):
    #     # calculate flops for 1 window with token length of N
    #     flops = 0
    #     # qkv = self.qkv(x)
    #     flops += N * self.dim * 3 * self.dim
    #     # attn = (q @ k.transpose(-2, -1))
    #     flops += self.num_heads * N * (self.dim // self.num_heads) * N
    #     #  x = (attn @ v)
    #     flops += self.num_heads * N * N * (self.dim // self.num_heads)
    #     # x = self.proj(x)
    #     flops += N * self.dim * self.dim
    #     return flops
class Lite_SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 fused_window_process=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        '''self.attn=BuildFormer.geoseg.models.BuildFormer.LWMSA(dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias)
        '''
        self.attn = Lite_WindowAttention(
            dim, window_size=self.window_size, num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)
        self.fused_window_process = fused_window_process

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            if not self.fused_window_process:
                shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
                # partition windows
                x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
            else:
                x_windows = WindowProcess.apply(x, B, H, W, C, -self.shift_size, self.window_size)
        else:
            shifted_x = x
            # partition windows
            x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C

        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)

        # reverse cyclic shift
        if self.shift_size > 0:
            if not self.fused_window_process:
                shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
                x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
            else:
                x = WindowProcessReverse.apply(attn_windows, B, H, W, C, self.shift_size, self.window_size)
        else:
            shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
            x = shifted_x
        x = x.view(B, H * W, C)
        x = shortcut + self.drop_path(x)

        # FFN
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops
class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops
class Lite_swinBasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 fused_window_process=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            Lite_SwinTransformerBlock(dim=dim if downsample == None else dim * 2,
                                      input_resolution=input_resolution if downsample == None else [
                                          input_resolution[0] // 2,
                                          input_resolution[
                                              1] // 2],
                                      num_heads=num_heads, window_size=window_size,
                                      shift_size=0 if (i % 2 == 0) else window_size // 2,
                                      mlp_ratio=mlp_ratio,
                                      qkv_bias=qkv_bias, qk_scale=qk_scale,
                                      drop=drop, attn_drop=attn_drop,
                                      drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                      norm_layer=norm_layer,
                                      fused_window_process=fused_window_process)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        # 先下采样
        if self.downsample is not None:
            x = self.downsample(x)
        # 再通过swin transformer
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops
class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x) # B Ph*Pw C

        return x


class LearnAttn_Net(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, img_size=512, patch_size=2, in_chans=3, num_classes=2,
                 embed_dim=64, depths=[2, 2, 6, 2], num_heads=[2, 4, 8, 16],
                 window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=True, patch_norm=True,
                 use_checkpoint=False, fused_window_process=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio



        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, 3, 512,512))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        self.depths = [32, 64, 128, 256, 512]

        # 全局特征
        self.layer1=LAttnBlock(32,3,self.depths[0])
        self.layer2 = LAttnBlock(32,self.depths[0], self.depths[1])
        self.layer3 = LAttnBlock(32,self.depths[1], self.depths[2])
        self.layer4 = LAttnBlock(32,self.depths[2], self.depths[3])



        # 卷积提取局部特征
        self.convlayers1 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                         ConvBNRelu(self.depths[0], self.depths[0]))
        self.convlayers2 = nn.Sequential(ConvBNRelu(self.depths[1], self.depths[1]),
                                         ConvBNRelu(self.depths[1], self.depths[1]))
        self.convlayers3 = nn.Sequential(ConvBNRelu(self.depths[2], self.depths[2]),
                                         ConvBNRelu(self.depths[2], self.depths[2]))
        self.convlayers4 = nn.Sequential(ConvBNRelu(self.depths[3], self.depths[3]),
                                         ConvBNRelu(self.depths[3], self.depths[3]))
        # 解码-----------------------------------------------



        # level1解码

        self.ConvTranspose1_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv1_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        # level2解码

        self.ConvTranspose2_1 = nn.ConvTranspose2d(self.depths[2], self.depths[1], 2, 2)
        self.Conv2_1 = nn.Sequential(ConvBNRelu(self.depths[1], self.depths[1]),
                                     ConvBNRelu(self.depths[1], self.depths[1]))
        self.ConvTranspose2_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv2_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))
        # level3解码


        self.ConvTranspose3_2 = nn.ConvTranspose2d(self.depths[3], self.depths[2], 2, 2)
        self.Conv3_2 = nn.Sequential(ConvBNRelu(self.depths[2], self.depths[2]),
                                     ConvBNRelu(self.depths[2], self.depths[2]))

        self.ConvTranspose3_1 = nn.ConvTranspose2d(self.depths[2], self.depths[1], 2, 2)
        self.Conv3_1 = nn.Sequential(ConvBNRelu(self.depths[1], self.depths[1]),
                                     ConvBNRelu(self.depths[1], self.depths[1]))

        self.ConvTranspose3_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv3_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        self.ConvAll = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        self.ConvOut = nn.Conv2d(self.depths[0], 2, 1, bias=True)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def forward_features(self, input):
        # 卷积提取局部特征
        # local0 = x=self.convlayers1(input)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local1 = x=self.convlayers2(x)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local2 =x=self.convlayers3(x)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local3 =x= self.convlayers4(x)


        # transformer提取整体特征

        if self.ape:
            x =input + self.absolute_pos_embed
        # x = self.pos_drop(x)

        levels=[]
        x=self.layer1(x)
        x=self.convlayers1(x)
        levels.append(x)

        x=F.max_pool2d(x,2,2)
        x = self.layer2(x)
        x = self.convlayers2(x)
        levels.append(x)

        x = F.max_pool2d(x, 2, 2)
        x = self.layer3(x)
        x = self.convlayers3(x)
        levels.append(x)

        x = F.max_pool2d(x, 2, 2)
        x = self.layer4(x)
        x = self.convlayers4(x)
        levels.append(x)


        # levels[0]=levels[0]+local0
        # levels[1] =levels[1]+ local1
        # levels[2] = levels[2] +local2
        # levels[3] =levels[3] + local3
        # 解码---------------------

        # level0的解码

        level0=levels[0]

        # level1的解码
        level1 = self.ConvTranspose1_0(levels[1])
        level1 = level1 + levels[0]
        level1 = self.Conv1_0(level1)


        # level2的解码

        level2 = self.ConvTranspose2_1(levels[2])
        level2 = level2 + levels[1]
        level2 = self.Conv2_1(level2)

        level2 = self.ConvTranspose2_0(level2)
        level2 = self.Conv2_0(level2)

        # level3的解码


        level3 = self.ConvTranspose3_2(levels[3])
        level3 = level3 + levels[2]
        level3 = self.Conv3_2(level3)

        level3 = self.ConvTranspose3_1(level3)
        level3 = level3 + levels[1]
        level3 = self.Conv3_1(level3)

        level3 = self.ConvTranspose3_0(level3)
        level3 = self.Conv3_0(level3)

        # 所有level的融合
        out = level0 + level1 + level2 + level3
        # out=self.ConvAll(out)
        out = self.ConvOut(out)

        return out

    def forward(self, x):
        x = self.forward_features(x)
        # x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops  ###
class LearnAttn_Net1(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, img_size=512, patch_size=2, in_chans=3, num_classes=2,
                 embed_dim=64, depths=[2, 2, 6, 2], num_heads=[2, 4, 8, 16],
                 window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=True, patch_norm=True,
                 use_checkpoint=False, fused_window_process=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio



        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, 3, 512,512))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        self.depths = [32, 32, 32, 32, 32]

        self.backbone= nn.Sequential(
            LAttnBlock(8, 3, self.depths[0]),
            LAttnBlock(8, self.depths[0], self.depths[1]),
            LAttnBlock(8, self.depths[1], self.depths[2]),
            LAttnBlock(8, self.depths[2], self.depths[3]),
            LAttnBlock(8, self.depths[3], self.depths[4])
        )




        # 解码-----------------------------------------------



        # level1解码

        self.ConvTranspose1_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv1_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        # level2解码

        self.ConvTranspose2_1 = nn.ConvTranspose2d(self.depths[2], self.depths[1], 2, 2)
        self.Conv2_1 = nn.Sequential(ConvBNRelu(self.depths[1], self.depths[1]),
                                     ConvBNRelu(self.depths[1], self.depths[1]))
        self.ConvTranspose2_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv2_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))
        # level3解码


        self.ConvTranspose3_2 = nn.ConvTranspose2d(self.depths[3], self.depths[2], 2, 2)
        self.Conv3_2 = nn.Sequential(ConvBNRelu(self.depths[2], self.depths[2]),
                                     ConvBNRelu(self.depths[2], self.depths[2]))

        self.ConvTranspose3_1 = nn.ConvTranspose2d(self.depths[2], self.depths[1], 2, 2)
        self.Conv3_1 = nn.Sequential(ConvBNRelu(self.depths[1], self.depths[1]),
                                     ConvBNRelu(self.depths[1], self.depths[1]))

        self.ConvTranspose3_0 = nn.ConvTranspose2d(self.depths[1], self.depths[0], 2, 2)
        self.Conv3_0 = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        self.ConvAll = nn.Sequential(ConvBNRelu(self.depths[0], self.depths[0]),
                                     ConvBNRelu(self.depths[0], self.depths[0]))

        self.ConvOut = nn.Conv2d(self.depths[0], 2, 1, bias=True)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def forward_features(self, input):
        # 卷积提取局部特征
        # local0 = x=self.convlayers1(input)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local1 = x=self.convlayers2(x)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local2 =x=self.convlayers3(x)
        # x = F.max_pool2d(x, 2, 2)
        #
        # local3 =x= self.convlayers4(x)


        # transformer提取整体特征

        if self.ape:
            x =input + self.absolute_pos_embed
        # x = self.pos_drop(x)

        levels=[]
        x=self.layer1(x)
        x=self.convlayers1(x)
        levels.append(x)

        x=F.max_pool2d(x,2,2)
        x = self.layer2(x)
        x = self.convlayers2(x)
        levels.append(x)

        x = F.max_pool2d(x, 2, 2)
        x = self.layer3(x)
        x = self.convlayers3(x)
        levels.append(x)

        x = F.max_pool2d(x, 2, 2)
        x = self.layer4(x)
        x = self.convlayers4(x)
        levels.append(x)


        # levels[0]=levels[0]+local0
        # levels[1] =levels[1]+ local1
        # levels[2] = levels[2] +local2
        # levels[3] =levels[3] + local3
        # 解码---------------------

        # level0的解码

        level0=levels[0]

        # level1的解码
        level1 = self.ConvTranspose1_0(levels[1])
        level1 = level1 + levels[0]
        level1 = self.Conv1_0(level1)


        # level2的解码

        level2 = self.ConvTranspose2_1(levels[2])
        level2 = level2 + levels[1]
        level2 = self.Conv2_1(level2)

        level2 = self.ConvTranspose2_0(level2)
        level2 = self.Conv2_0(level2)

        # level3的解码


        level3 = self.ConvTranspose3_2(levels[3])
        level3 = level3 + levels[2]
        level3 = self.Conv3_2(level3)

        level3 = self.ConvTranspose3_1(level3)
        level3 = level3 + levels[1]
        level3 = self.Conv3_1(level3)

        level3 = self.ConvTranspose3_0(level3)
        level3 = self.Conv3_0(level3)

        # 所有level的融合
        out = level0 + level1 + level2 + level3
        # out=self.ConvAll(out)
        out = self.ConvOut(out)

        return out

    def forward(self, x):
        x = self.forward_features(x)
        # x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops  ###

